| """Basic usage example for the topic analysis pipeline.""" |
|
|
| import sys, os |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) |
|
|
| from src.topic_modeler import run_topic_model |
| from src.models import ModelType |
|
|
| docs = [ |
| "The wait time was over two hours and nobody explained the delay.", |
| "Dr Smith was very thorough and took time to explain my diagnosis clearly.", |
| "The reception staff were rude and unhelpful when I called to reschedule.", |
| "My medication was changed without any explanation and I had side effects.", |
| "The nurses were incredibly kind and made me feel comfortable during my stay.", |
| "The hospital parking is expensive and there are never enough spaces.", |
| "The specialist explained everything patiently and answered all my questions.", |
| "Billing department sent me an incorrect invoice and took weeks to fix it.", |
| "The follow-up appointment was easy to book and the doctor remembered my case.", |
| "I was not informed about the procedure risks beforehand.", |
| ] |
|
|
| result = run_topic_model(docs, ModelType.BERTOPIC_MINI) |
|
|
| print(f"Model: {result.model_type}") |
| print(f"Topics: {result.num_topics}") |
| print(f"Outliers: {result.outlier_count}\n") |
|
|
| for t in result.topics: |
| print(f"Topic {t.topic_id} ({t.doc_count} docs): {', '.join(t.keywords[:6])}") |
|
|
| print() |
| for d in result.documents: |
| print(f" Doc {d.doc_id:2d} → Topic {d.topic_id:2d} ({d.probability:.1%}) {d.text[:60]}…") |
|
|
